The present disclosure relates in general to natural language processing question & answer (NLP Q&A) systems. More specifically, the present disclosure relates to NLP Q&A systems tailored to emulate answers that would be provided by a predetermined entity or by a predetermined cluster of blended entities.
It is known to provide NLP Q&A systems that answer natural language questions by querying data repositories and applying elements of language processing, information retrieval and machine learning to arrive at a conclusion. Such systems are able to assist humans with certain types of semantic query and search operations, such as the type of natural question-and-answer paradigm of a medical environment. An example NLP Q&A system is IBM's DeepQA technology described in U.S. Pat. No. 8,275,803, issued Sep. 25, 2012, which is assigned to the assignee of the present disclosure, and which is incorporated by reference herein in its entirety.
DeepQA systems and methodologies have been developed that are able to understand complex questions input to the system in natural language, and are able to answer the questions with enough precision, confidence, and speed to augment human handling of the same questions within a given environment, such as a medical inquiry and diagnostic paradigm where time-to-answer is of the essence. NLP Q&A systems such as IBM's DeepQA technology often used unstructured information management architecture (UIMA), which is a component software architecture for the development, discovery, composition, and deployment of multi-modal analytics for the analysis of unstructured information and its integration with search technologies developed by IBM.
Because NLP Q&A systems obtain “knowledge” by accessing and processing information, it is generally accepted that feeding more information into such systems has the potential to increase the scope of the system's knowledge and improve the quality of the conclusions/answers the system provides. However, there are practical limitations on the ability to feed more information to a system. For example, some information for a variety of reasons may simply be inaccessible. Even if accessible, accessing the information often comes at a cost, which may be a direct financial cost (e.g., fees for access to a database) or the time-cost and/or computing resource cost of ingesting and analyzing more information.